/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.ml;

import org.apache.spark.ml.feature.BucketedRandomProjectionLSH;
import org.apache.spark.ml.feature.BucketedRandomProjectionLSHModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

import java.util.Arrays;
import java.util.List;

// $example on$
// $example off$

public class JavaBucketedRandomProjectionLSHExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaBucketedRandomProjectionLSHExample")
                .getOrCreate();
        
        // $example on$
        List<Row> dataA = Arrays.asList(
                RowFactory.create(0, Vectors.dense(1.0, 1.0)),
                RowFactory.create(1, Vectors.dense(1.0, -1.0)),
                RowFactory.create(2, Vectors.dense(-1.0, -1.0)),
                RowFactory.create(3, Vectors.dense(-1.0, 1.0))
        );
        
        List<Row> dataB = Arrays.asList(
                RowFactory.create(4, Vectors.dense(1.0, 0.0)),
                RowFactory.create(5, Vectors.dense(-1.0, 0.0)),
                RowFactory.create(6, Vectors.dense(0.0, 1.0)),
                RowFactory.create(7, Vectors.dense(0.0, -1.0))
        );
        
        StructType schema = new StructType(new StructField[]{
                new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
                new StructField("keys", new VectorUDT(), false, Metadata.empty())
        });
        Dataset<Row> dfA = spark.createDataFrame(dataA, schema);
        Dataset<Row> dfB = spark.createDataFrame(dataB, schema);
        
        Vector key = Vectors.dense(1.0, 0.0);
        
        BucketedRandomProjectionLSH mh = new BucketedRandomProjectionLSH()
                .setBucketLength(2.0)
                .setNumHashTables(3)
                .setInputCol("keys")
                .setOutputCol("values");
        
        BucketedRandomProjectionLSHModel model = mh.fit(dfA);
        
        // Feature Transformation
        model.transform(dfA).show();
        // Cache the transformed columns
        Dataset<Row> transformedA = model.transform(dfA).cache();
        Dataset<Row> transformedB = model.transform(dfB).cache();
        
        // Approximate similarity join
        model.approxSimilarityJoin(dfA, dfB, 1.5).show();
        model.approxSimilarityJoin(transformedA, transformedB, 1.5).show();
        // Self Join
        model.approxSimilarityJoin(dfA, dfA, 2.5).filter("datasetA.id < datasetB.id").show();
        
        // Approximate nearest neighbor search
        model.approxNearestNeighbors(dfA, key, 2).show();
        model.approxNearestNeighbors(transformedA, key, 2).show();
        // $example off$
        
        spark.stop();
    }
}
